System and method for providing closed loop infusion formulation delivery

ABSTRACT

A system and method for providing closed loop infusion formulation delivery which accurately calculates a delivery amount based on a sensed biological state by adjusting an algorithm&#39;s programmable control parameters. The algorithm calculates a delivery amount having proportional, derivative, and basal rate components. The control parameters may be adjusted in real time to compensate for changes in a sensed biological state that may result from daily events. Safety limits on the delivery amount may be included in the algorithm. The algorithm may be executed by a computing element within a process controller for controlling closed loop infusion formulation delivery. The biological state is sensed by a sensing device which provides a signal to the controller. The controller calculates an infusion formulation delivery amount based on the signal and sends commands to an infusion formulation delivery device which delivers an amount of infusion formulation determined by the commands.

CROSS-REFERENCE TO RELATED APPLICATIONS

Embodiments of the present invention claim priority from a U.S. Provisional Application entitled “A System and Method for Providing Closed Loop Infusion Formulation Delivery,” Ser. No. 60/335,664, filed Oct. 23, 2001, the contents of which are incorporated by reference herein. Also, the present application relates to co-pending U.S. Provisional Application entitled “Safety Limits For Closed-Loop Infusion Pump Control,” Ser. No. 60/318,062, Attorney Docket No. 047711-0264, the content of which is incorporated by reference herein.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates generally, to infusion pump systems for the delivery of infusion formulations, and in particular, to a closed-loop algorithm for use in conjunction with a process controller for controlling the delivery of an infusion formulation to a body based in part on sensed blood

2. Description of Related Art

Infusion pumps have been used for the programmed delivery of measured doses of an infusion formulation. (An infusion formulation is defined in the present disclosure as the substance being delivered by the infusion pump. This substance may comprise either a mixture of different components or it may be a single, pure substance, including, but not limited to drugs, dyes or other indicators, nutrient, or the like.) A typical example of such use is the delivery of an insulin formulation to a patient.

In the case where the infusion formulation is an insulin formulation, a sensing device may regulate the delivery of the insulin formulation by sensing the levels of blood glucose in the person. The delivery of the insulin formulation may be controlled by a control device associated with the pump having as an input a sensed blood glucose level. The control device may control activation of the pump to deliver an appropriate amount of the insulin formulation in accordance with the sensed blood glucose level.

Insulin is a protein hormone normally formed within the human pancreas. Because it regulates carbohydrate (sugar) metabolism, insulin is required for normal metabolic function. More specifically, insulin helps the body metabolize glucose. To avoid medical problems such as hypoglycemia and hyperglycemia, blood glucose levels should be maintained within a specific range. A normal range for glucose in the human body may be between 85 and 120 milligrams/deciliter (mg/dl).

In a non-diabetic person, insulin is secreted by the pancreas in small amounts throughout the day (basal rate of insulin secretion). In addition, the amount of insulin secreted by the pancreas may be modified under certain circumstances. For example, the pancreas of a non-diabetic person normally secretes larger amounts of insulin (bolus rate of insulin secretion) when the person ingests a meal to prevent postprandial hyperglycemia, i.e., abnormally increased sugar content in the blood.

In contrast to the non-diabetic person, a diabetic person's pancreas may not secrete the required amount of insulin. Thus, the diabetic person has to somehow artificially introduce the insulin into the body. One method of introducing the insulin is by the conventional insulin formulation injection method using a syringe. Using this method, the body's blood glucose level may be monitored (for example, by checking a blood sample) and the amount of insulin to be injected may be adjusted accordingly. For example, after a meal the blood glucose level may be monitored and an appropriate amount of insulin may be injected into the bloodstream of the user.

In the alternative, a diabetic person may choose to use an infusion pump such as the infusion pump described above. By using an infusion pump, a diabetic person may be able to adjust insulin delivery rates for the pump in accordance with the user's needs. These needs may be determined based on prior experience and/or the results of glucose monitoring (for example, by a sensing device in combination with a communication device).

In addition, infusion pumps may be engineered to function as an artificial pancreas. Such an infusion pump may deliver a specific amount of insulin formulation at specific intervals. As discussed above, a sensing device associated with the pump may monitor the blood glucose level of the user and the blood glucose level may then be used by the pump to automatically regulate the delivery of the insulin formulation.

It is known to use as a control device a process controller for performing automatic regulation of the infusion pump. The process controller, for example a processor or other computing element, controls the process such that a process variable is maintained at a desired set point value (also referred to in the present-disclosure as the “goal”). Such process controllers typically use a set of control parameters which have been determined through, for example, experimentation or calculation, to operate in an optimal manner to control the process variable. Although not the only possible technique, these control parameters are typically dependent on the anticipated range of differences (“error values”) that result between the process variable and the set point during actual operation of the process.

Ordinarily, infusion formulation delivery systems utilize control systems having an input-response relationship. A system input, such as a sensed biological state, produces a physiological response related to the input. Typically, the input (such as a sensed blood glucose level) is used to control some parameter associated with the response variable (such as an insulin infusion rate or an amount of insulin).

A process controller employed in the delivery of an insulin formulation typically executes a closed-loop algorithm that accepts and processes a blood glucose level input supplied to the controller by a sensing device. The closed-loop algorithm may adjust insulin formulation delivery as a function of, for example, the rate of change over time of the sensed glucose level.

These closed-loop algorithms have many limitations. Some of these limitations result from the fact that a process controller employing a closed-loop algorithm to control the delivery of an insulin formulation may be restricted to only adding insulin formulation to the system. Once insulin formulation is added to the system, normally the controller cannot retrieve it.

Additional limitations result from the fact that certain parameters affecting glucose production may not be adequately compensated for by these closed-loop algorithms. For example, certain daily events may significantly affect glucose production levels in the human body. Thus, these events may also significantly affect the amount of insulin required to metabolize the glucose.

Exercise, for example, has been shown to lower blood glucose levels in the human body. Thus, exercise may result in a dip in blood glucose levels and a corresponding decrease in the amount of insulin formulation delivered by the body. Longer or more strenuous exercise events may result in a greater dip in blood glucose level than shorter and less strenuous exercise events.

Similarly, sleep and stress may affect the body's ability to burn carbohydrates and therefore may affect glucose levels. For example, glucose metabolism has been found to be slower in a sleep deprived state. In addition, elevations of certain stress hormones within the body may also result in slower glucose metabolism. Thus, longer or shorter periods of sleep or stress may result in more or less significant changes in glucose levels.

Furthermore, the ingestion of certain medications may affect a user's sensitivity to insulin, i.e. a given amount of insulin may be more or less sufficient depending on whether or not a particular medication has been taken.

An additional event that may significantly affect the production of glucose in the body is the ingestion of food. This results in part from the fact that during digestion carbohydrates are broken down into glucose that then enters the bloodstream. In addition, the amount and type of foods ingested affect the amount of glucose produced.

Closed-loop algorithms employed for controlling delivery of an insulin formulation in response to sensed blood glucose levels may not adequately compensate for the affects such daily events may have on blood glucose levels. Thus, the diabetic person relying on such closed-loop algorithms may be at an increased risk of hypoglycemia and/or hyperglycemia.

SUMMARY OF THE DISCLOSURE

Therefore, it is an advantage of embodiments of the present invention to provide a closed-loop algorithm for controlling delivery of insulin formulation which more accurately calculates an infusion formulation delivery rate based on a level of blood glucose which is sampled in a body at predefined intervals.

It is a further advantage of embodiments of the present invention to provide a closed-loop algorithm for controlling delivery of insulin formulation which may be adjusted in real time to more accurately determine whether a blood glucose level is rising or falling over a predetermined interval.

It is a further advantage of embodiments of the present invention to provide safety limits for bolus delivery that may be compared with samples of blood glucose parameters at predefined intervals and which enable or disable bolus delivery based on the comparisons.

It is a further advantage of embodiments of the present invention to provide safety limits on the amount of insulin formulation that may be stored in an accumulator during a predefined time interval.

It is a further advantage of embodiments of the present invention to provide safety limits on the amount of insulin formulation that may be delivered to a user during a predefined time interval.

These and other advantages are accomplished according to embodiments of a closed-loop algorithm for use in conjunction with a process controller for delivering an infusion formulation. Components of the closed-loop algorithm calculate a present value of infusion formulation in a body as well as whether that value is rising or falling overall during a predefined time interval. The closed-loop algorithm includes an equation whose variables are programmable in real time. The variables may be used as control parameters which may be adjusted to adjust the algorithm to more accurately calculate the present value of infusion formulation in the body.

Preferred embodiments of the present invention provide a closed-loop algorithm for use with a proportional-derivative controller for delivering an insulin formulation which comprises an equation for calculating a proportional component, a derivative component, and a basal component of an amount of insulin formulation to be delivered based on a sensed blood glucose level. Control parameters within the closed-loop algorithm may be programmable in real time and may be adjusted to compensate for events which may significantly affect the blood glucose level.

Depending upon the context of use, the invention may include various combinations of these features which function together to provide both adjustable control parameters and safety limits on the delivery of infusion formulation in response to a detected biological state. Various embodiments of the invention include one or more of these features.

These and other objects, features, and advantages of embodiments of the invention will be apparent to those skilled in the art from the following detailed description of embodiments of the invention, when read with the drawings and appended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a block diagram of an infusion formulation delivery system utilizing a control system having an input-response relationship, according to preferred embodiments of the invention;

FIG. 2 shows a flow diagram of a general process performed by a closed-loop algorithm for adjusting infusion formulation delivery as a function of a change in a sensed biological state;

FIG. 3 shows the operation of a closed-loop algorithm used by a proportional-derivative controller;

FIG. 4 shows a flow diagram 400 illustrating a process for implementing a filter order, according to an embodiment of the invention;

FIG. 5A shows a blood glucose response curve after a higher filter order for the falling side of the curve has been implemented, according to one embodiment of the present invention;

FIG. 5B shows a magnified view of a portion of the response curve of FIG. 5A;

FIG. 6 shows flow diagram 600 to illustrate effects of implementing time windows, according to an embodiment of the invention;

FIG. 7 shows a graph of a human blood glucose response for a user who has ingested a meal, illustrating effects of implementing a time window, according to an embodiment of the invention;

FIG. 8 shows a graph of a human blood glucose response for a user who has ingested a meal, illustrating effects of implementing time windows, according to an embodiment of the invention;

FIG. 9 shows flow diagram which illustrates effects of increasing the value of x in the trend term of Equation 4 when the trend term first indicates that the blood glucose level is falling, according to an embodiment of the invention;

FIG. 10 shows a flow diagram illustrating effects of a programmable trend gain on the present calculated value of the infusion formulation, according to an embodiment of the invention;

FIG. 11 shows a graph of a human blood glucose response for a user who has ingested a meal, illustrating a trend up gain and a trend down gain, according to an embodiment of the invention;

FIG. 12 shows a flow diagram illustrating effects of disabled and enabled trend terms, according to an embodiment of the invention;

FIG. 13 shows a graph of a human blood glucose response for a user who has ingested a meal, illustrating effects of disabled and enabled trend terms, according to an embodiment of the invention;

FIG. 14 shows, a flow diagram illustrating effects of the basal rate component, according to an embodiment of the invention;

FIG. 15 shows a graph of a human blood glucose response for a user who has ingested a meal, illustrating effects of the basal rate component, according to an embodiment of the invention;

FIG. 16A shows a graph of a human blood glucose response for a user who has ingested a meal, illustrating a process whereby a pump stroke volume is accumulated, according to an embodiment of the invention;

FIG. 16B shows a magnified view of a portion of the response curve of FIG. 16A; and

FIG. 17 shows a flow diagram illustrating a verification of the status of each bolus control parameter before a bolus delivery is executed, according to an embodiment of the invention.

DETAILED DESCRIPTION OF EMBODIMENTS OF THE INVENTION

In the following description of preferred embodiments, reference is made to the accompanying drawings which form a part hereof, and in which is shown by way of illustration specific embodiments in which the invention may be practiced. It is to be understood that other embodiments may be utilized and structural changes may be made without departing from the scope of preferred embodiments of the present invention.

Environment of Use

As discussed above, embodiments of the present invention relate to a closed-loop algorithm for use in conjunction with a process controller for controlling the delivery of an infusion formulation to a body based in part on a sensed biological state within the body.

Embodiments of the invention may be employed in various infusion environments including, but not limited to a biological implant environment. In preferred embodiments, the closed-loop algorithm is employed for use in conjunction with a delivery device such as an infusion pump utilized in an implant environment within a human body. However, other embodiments may be employed for use in other biological implant or non-implant environments, including but not limited to external infusion devices, pumps or the like.

Furthermore, in example embodiments described herein, the closed-loop algorithm is employed for use in conjunction with an infusion pump configured for delivery of an insulin formulation used to regulate glucose levels in a diabetic user. However, other embodiments may be employed in the delivery of other infusion formulations having other pharmacological properties.

Closed-Loop Control System

A block diagram of an infusion formulation delivery system 100 utilizing a control system having an input-response relationship according to preferred embodiments of the invention is shown in FIG. 1. A sensor 102 generates a sensor signal 112 representative of a system parameter input 110 (such as a blood glucose level of a human body 108), and provides the sensor signal 112 to a controller 104. The controller 104 receives the sensor signal 112 and generates commands 114 that are communicated to the infusion formulation delivery device 106. The infusion formulation delivery device 106 then delivers the infusion formulation output 116 to the body 108 at a determined rate and amount in order to control the system parameter 110.

Sensor 102 may comprise a sensor, sensor electrical components for providing power to the sensor and generating the sensor signal 112, a sensor communication system for carrying the sensor signal 112 to controller 104, and a sensor housing for enclosing the electrical components and the communication system. Controller 104 may include one or more programmable processors, logic circuits, or other hardware, firmware or software components configured for implementing the control functions described herein, a controller communication system for receiving the sensor signal 112 from the sensor 102, and a controller housing for enclosing the controller communication system and the one or more programmable processors, logic circuits, or other hardware, firmware or software components. The infusion formulation delivery device 106 may include a suitable infusion pump, infusion pump electrical components for powering and activating the infusion pump, an infusion pump communication system for receiving commands from the controller 104, and an infusion pump housing for enclosing the infusion pump, infusion pump electrical components, and infusion pump communication system.

Closed-Loop Algorithm

FIG. 2 shows a flow diagram of a general process performed by a closed-loop algorithm for adjusting infusion formulation delivery as a function of, for example, the rate of change over time of a sensed biological state. As shown in step 202, the closed-loop algorithm checks for changes in the biological state at timed intervals. A sensing device such as sensor 102 detects the change in glucose level and communicates the change to a control device such as controller 104 as an input to the closed-loop algorithm. If no change is detected, the closed-loop algorithm loops back to step 202, repeating this process until a change is detected. When a change occurs at step 204, the closed-loop algorithm determines the amount and/or rate of infusion formulation required based on the input and various parameters that have been programmed into the controller.

Where the infusion formulation delivery system 100 shown in. FIG. 1 includes a controller 104 used for controlling an insulin response to a sensed blood glucose level, the closed-loop algorithm may be of the proportional-derivative (PD) type. The use of a PD type closed-loop algorithm is advantageous, for example, when processing resources such as processor power and/or memory may be limited. In alternative embodiments, a proportional-integral-derivative (PID) type closed-loop algorithm may be used.

PD controllers may utilize a closed-loop algorithm which computes both a proportional component and a derivative component of a response (output) to changes in a system parameter (input). For example, the proportional and derivative components may be combined to calculate an amount of insulin formulation to be delivered in response to a present sensed blood glucose level (system parameter input 110) within a body 108. The controller may then issue commands 114 to, for example, output a calculated amount of insulin formulation (output 116) to an infusion site on or within the body 108 based on the present sensed blood glucose level.

The magnitude of each component's contribution to the calculated amount of insulin formulation to be delivered to the infusion site may be expressed by a formula or equations, such as the following equations:

U _(P)=α(G _((t)) −G _(sp))   Equation 1

and

U _(D)=βdG/dt,   Equation 2

where

U_(P) is the proportional component of the response,

U_(D) is the derivative component of the response,

α is a proportional gain coefficient,

β is a derivative gain coefficient,

G is a present blood glucose level,

G_(sp) is a desired blood glucose level or “set point” for the blood glucose level, and

t is the time at which the blood glucose level is sensed.

There is a desired blood glucose level G_(sp) for each person which may be determined, for example, from experimentation or from the person's historical physiological data. The closed-loop control system may be designed to maintain the desired blood glucose level G_(sp) for a particular person. It may do this, in part, by measuring the difference between the determined G_(sp) and a blood glucose level G sensed at time t (G_((t))). This difference is the blood glucose level error at time t that must be corrected.

The proportional component expressed in Equation 1 determines whether the blood glucose level error is positive, negative, or zero, (i.e., whether G_((t)) is, respectively, higher, lower, or equal to G_(sp)). Thus, G_(sp) is subtracted from G_((t)). If G_((t)) is higher than G_(sp), the controller 104 may generate an insulin formulation delivery command 114 to drive the infusion formulation delivery device 106 to provide insulin formulation (output 116) to the body 108. If G_((t)) is lower than G_(sp), the controller 104 may reduce or stop delivery of the insulin formulation to the body 108 by the infusion formulation delivery device 106. The result of subtracting G_(sp) from G_((t)) is then multiplied by a proportional gain coefficient α. The derivative component dG/dt expressed in Equation 2 determines if the blood glucose level is presently rising or falling and at what rate of change.

Thus, to determine the amount of infusion formulation to be delivered at any point in time (I_((t))), the following standard equation may be used:

I _((t))=α(G _((t)) −G _(sp))+βdG/dt   Equation 3

where I_((t)) is the amount of insulin formulation to be delivered based on the sensed blood glucose level at time t.

Example Operation Of A Closed-Loop Algorithm

Referring now to FIG. 3, the operation of a closed-loop algorithm used by a PD controller is described. FIG. 3 illustrates a typical human blood glucose response to the ingestion of a meal. Shown in FIG. 3 is a graph of a blood glucose response curve 300 (on the y axis) as a function of time (on the x axis). This blood glucose response curve 300 is representative of blood glucose levels sensed at various sampling times as a system parameter 110 by a sensor 102, as shown in FIG. 1.

As shown in FIG. 3, after a person ingests a meal 302, there is typically a steady rise 304 in blood glucose level over time until the blood glucose level reaches a peak 306. It has been observed from experimentation that peak 306 may occur approximately 90 minutes after ingestion of the meal. After peak 306 has been reached, it has been observed that the blood glucose level then begins to decrease 308 over time. During the decline from the first peak 306, a second temporary rise 310 in blood glucose level has been observed. A second peak 312 results from this temporary rise 310. This second peak 312 may occur approximately 30 to 90 minutes after the occurrence of peak 306 and typically tends to occur 30 to 60 minutes after the occurrence of peak 306.

After peak 312 has been reached, it has been observed that the blood glucose level then continues as before to decrease 314 over time. Although the reasons for this second, temporary rise 310 are not completely understood at the present time, it is a consistently observable phenomenon that presents a problem for a closed-loop algorithm.

To understand the problem, it is helpful to understand the response of a closed-loop algorithm at the various points of the response curve 300 shown in FIG. 3. As stated above, at point 302, the meal is ingested. As the blood glucose level rises 304 above the set point 316, a closed-loop algorithm may calculate both the amount by which the present blood glucose level exceeds the set point value (a proportional component) and may also determine that the blood glucose level is rising at a certain rate (a derivative component). Thus, a closed-loop algorithm may calculate a result based on these two components which causes a command to issue from a controller associated with the algorithm to deliver a calculated amount of insulin at a time t on the response curve 300 corresponding to 304.

At peak 306 of the response curve 300, the blood glucose level is neither rising nor falling, but the proportional component calculates that it is still above the set point and therefore the controller associated with the closed-loop algorithm may continue to issue commands to deliver more insulin formulation, although it may not be as large an amount as that issued at 304 on the response curve 300.

At 308, the proportional component calculates that the blood glucose level is still above the set point. However, now the blood glucose level is falling, and therefore the controller associated with the closed-loop algorithm may issue commands to deliver a decreased amount of insulin formulation based on the calculation of the derivative component.

At 310, the proportional component calculates that the blood glucose level is still above the set point. The derivative component will calculate that the blood glucose level is rising again. At this point, the controller associated with the closed-loop algorithm may issue a command to deliver another significant amount of insulin based on this information although, seen globally, the blood glucose level is decreasing overall. Thus, because of this additional input of insulin formulation into the system, the risks of hypoglycemia to the user are increased.

Embodiments Of Closed-Loop Algorithms

Preferred embodiments of the present invention address the limitations of a closed-loop algorithm exemplified above in relation to FIG. 3. Preferred embodiments of closed-loop algorithms more accurately determine the amount of insulin formulation to be delivered based on a sensed blood glucose level by including programmable control parameters which may be used to introduce discontinuities in the calculation of I_((t)) unlike the continuous calculations of I_((t)) performed by the closed-loop algorithm described above. Embodiments of the present invention may be more effective at maintaining a desired blood glucose level for a particular user under circumstances where blood glucose level may be significantly affected by events such as, but not limited to meals, sleep, and exercise. As a result, the risk of hypoglycemia and/or hyperglycemia in the user may be reduced.

In some embodiments of the present invention, the derivative component of the closed-loop algorithm (dG/dt) shown in Equation 2 above is referred to as the “trend term” and may be expressed, as:

Trend term=(G _((t)) −G _((t-x)))   Equation 4

where x is a numerical value representing an increment of time.

In some embodiments, the value of the trend term is calculated at predetermined intervals, for example each minute, and is used to determine the “trend” of G, i.e., whether the value of G is trending up or trending down during a timeframe determined by the term (t−x). Thus, by changing the value of x, the timeframe for sampling the trend may be lengthened or shortened. As an example, using Equation 4, if x=10 minutes, the blood glucose level sensed 10 minutes prior in time to time t is subtracted from the blood glucose level sensed at time t. In some embodiments, as discussed in more detail below, the value of x may be programmable. In alternative embodiments, linear regression or other curve-fitting techniques may be used.

Generally, a shorter timeframe (and, thus, a smaller value of x) is preferred for trend calculation because the shorter the timeframe, the more responsive the infusion formulation delivery system may be to a rising or falling blood glucose level. However, this responsiveness must be balanced against noise susceptibility of the sensor signal, which may increase as the timeframe gets shorter. After the trend term is calculated, it is multiplied by the derivative gain coefficient β.

The proportional gain coefficient a and derivative gain coefficient β (β is also referred to in the present disclosure as the “trend gain”) may be chosen based, for example, on experimentation. As an example, they may be chosen based on observations of the insulin response of several, normal glucose tolerant users. An average of the values of these responses may then be taken. Alternatively, other statistical values besides an average value may be used, for example a maximum or minimum value, standard deviation value, or some other suitable value.

In some embodiments, as discussed in more detail below, both the proportional and derivative gain coefficients may be programmable. In addition, β may be programmed as one value when the trend is going up and a different value when the trend is going down (also referred to in the present disclosure as the “trend up” and “trend down” gains).

It is believed that even if G_((t)) is equal to G_(sp) (in other words if the proportional component of the response is zero), a certain minimal amount of insulin formulation should still be delivered in order to maintain that condition. Thus, in some embodiments, in addition to Equation 1 and Equation 2 shown above, a basal insulin formulation delivery amount is included as a further component of the response. This basal component (B₀) represents, in some embodiments, a minimum amount of insulin formulation that would be delivered when G_((t)) is equal to or greater than G_(sp) (i.e., when the blood glucose level at time t is equal to or greater than the desired blood glucose level or set point) and without regard to the rate at which the blood glucose level is rising or falling. In some embodiments, as discussed in more detail below, B₀ may be programmable and may be selected from a programmable table of multiple B₀ values based on certain criteria. By selecting B₀ values from this programmable table, different values of B₀ may be selected for different parts of the day (for example, dawn). Thus, different parts of the day may be treated differently than other parts of the day.

Thus, to determine the amount of infusion formulation to be delivered at any point in time (I_((t))) the following equation may be used by embodiments of the present invention:

I _((t))=α(G _((t)) −G _(sp))+β((G _((t)) −G _((t-x)))/x)+B₀   Equation 5

Higher Order Filters For Down Trend

Generally, the body's blood glucose level changes slowly compared to the rate at which the sensor 102 samples these levels. Therefore, high frequency signal components are typically noise. Referring again to FIG. 1, in some embodiments of the present invention sensor 102 may further include a filter. The filter may be used to reduce noise seen in sensor signal 112 in particular frequency bands prior to being received by controller 104. In some embodiments, a low pass filter such as, but not limited to, a finite impulse response (“FIR”) filter, is used for this purpose. This filter may be adjusted to pass lower frequencies and stop higher frequencies.

By increasing the order of the FIR filter, a sharper cutoff in the frequency response of the low pass filter may be achieved. In one embodiment of the present invention, the order of the filter may be programmable and different orders of the filter may be implemented based on whether the blood glucose level response curve (for example, response curve 300 in FIG. 3) is rising or falling.

FIG. 4 shows a flow diagram 400 illustrating the process for implementing a filter order. As illustrated in flow diagram 400, in one embodiment the derivative component of Equation 5 may be sampled at step 402. If the derivative component of Equation 5 is a positive value or zero, i.e., if the blood glucose level is rising or at a peak, the filter order may be maintained as shown in step 404. If the derivative component of Equation 5 is a negative value, i.e., if the blood glucose level is falling, a higher order filter may be implemented at step 406. As a result of implementing a higher order filter when the blood glucose level is falling, the temporary peaks on the falling side of the response curve (such as peak 312 in FIG. 3) may be flattened, as illustrated in FIGS. 5A and 5B.

FIGS. 5A and 5B illustrate the effects of this embodiment of the present invention on a response curve such as response curve 300. FIG. 5A shows a response curve 500 after the higher filter order for the falling side has been implemented according to one embodiment of the present invention described above. FIG. 5B shows a magnified view of a portion of the response curve referred to in FIG. 5A by numeral 518.

It can be seen from FIG. 5B that the second peak 512 (corresponding to second peak 312 in FIG. 3) has been flattened as a result of the higher order filter. Thus, the derivative component of the closed-loop algorithm may not detect as steep a rise and may reduce the amount of insulin formulation delivered as a result of this second peak 512. Therefore, as a result of implementing embodiments of the invention, the risk of hypoglycemia to the user may be reduced.

Disabling Closed-Loop Algorithm During Predefined Time Window

In another embodiment of the present invention, after a meal has been ingested by a user, the amount of insulin formulation to be delivered based on a sensed blood glucose level may be more accurately determined by establishing, for example from historical physiological data, a time window within which the temporary rise in blood glucose level occurs in the user. Once this time window has been established, embodiments of the present invention may disable any further commands from issuing from the controller (for example, commands 114 from controller 104 in FIG. 1), by, for example, programming start and stop times for the time window that may be used by the controller to suspend any further calculations of I_((t)) during the time window.

FIG. 6 shows flow diagram 600 which illustrates the effects of implementing time windows, as described above. As illustrated in flow diagram 600, in one embodiment the current time t may be sampled and compared at step 602 to the programmed start and stop times to determine if time t is within the programmed time window. If time t is not within the programmed time window, the issuance of commands based on Equation 5 may be enabled at step 604. If time t is within the programmed time window, the issuance of commands based on Equation 5 may be disabled at step 606 until the programmed stop time. In this way, minimal or no additional insulin formulation may be delivered during the time window, as illustrated by the graph shown in FIG. 7.

FIG. 7 shows a graph of a human blood glucose response 700 for a user who has ingested a meal at the point-in time referred to by numeral 702. For the purposes of illustration, it will be assumed that it has been established from the user's historical physiological data that the second rise occurs in the user at the time referred to by numeral 724. Thus, in the present example, the second peak 712 occurs approximately two hours after the meal is ingested. Thus, the time window for disabling commands from being issued by the controller may be set between a disable start time, referred to by numeral 726, and a disable stop time, referred to by numeral 728. After time 728 is reached, the controller commands may again be enabled.

It can be seen from FIG. 7 that because the second rise 710 and resulting second peak 712 occur within the programmed time window, the second rise does not result in any increase in delivered insulin formulation. This discontinuity in the calculation of I_((t)) may thus cause I_((t)) to be calculated based only on the global downward trend of response curve 700. Therefore, as a result of implementing one embodiment of the invention, the temporary rise 710 does not cause any increase in the amount of delivered insulin formulation, and the risk of hypoglycemia to the user is reduced.

Programmable Control Parameters For Equation 5

In yet another embodiment of the present invention, the amount of insulin formulation to be delivered based on a sensed blood glucose level may be more accurately determined by having control parameters in Equation 5 which are programmable. In some embodiments, higher accuracy is achieved by including some control parameters which may be programmable in real time, i.e., while the closed-loop control system is in operation. Table 1 shows the control parameters within Equation 5 that may be programmable in different embodiments of the present invention. In some embodiments, all the control parameters shown in Table 1 are programmable. In one embodiment, the control parameters shown in Table 1 may be programmed in real time. Table 1 also includes example values for each control parameter.

TABLE 1 Control Parameter Value Glucose Set Point (G_(sp)) 100 mg/dl Basal Rate (B₀) 0.5 units/hour Proportional Gain (α) 0.01 units/hour Trend Term 2 mg/dl/minute Trend Up Gain (β) 1.0 units/hour * (mg/dl/minute) Trend Down Gain (β) 3.0 units/hour * (mg/dl/minute)

Some embodiments of the present invention use the programmable control parameters shown in Table 1 to advantageously adjust the closed-loop algorithm to compensate for changes in the blood glucose level that result from events such as, but not limited to, a meal event. The temporary rise in blood glucose level seen a period of time after the meal has been ingested is an example of a change in blood glucose level resulting from an event. Other events that may require compensation for changes in the blood glucose level include, but are not limited to exercise, illness, stress, sleep and other events which may induce metabolic changes. Some embodiments may adjust the control parameters to compensate for the temporary rise so that it does not result in the delivery of a significant amount of insulin formulation. Thus, these embodiments decrease the risks of hypoglycemia to the user.

In one embodiment, the timeframe of the trend term of Equation 4 may be lengthened by increasing the programmable value of x. This embodiment is illustrated by the graph shown in FIG. 8, which shows a human blood glucose response 800 for a user who has ingested a meal at the point in time referred to by numeral 802. A first timeframe wherein x=10 minutes is referred to by numeral 804 and defines a 10 minute timeframe extending back in time from time t. It can be seen that if a trend term is calculated at time t, the trend of the blood glucose level will be calculated as rising 808 for that defined timeframe.

By increasing the value of x in the trend term, the timeframe may be lengthened in order to decrease the responsiveness of the infusion formulation delivery system and calculate a trend term that is more accurate in terms of whether the blood glucose level is globally rising or falling.

This is illustrated by a second timeframe, referred to by numeral 806, wherein x=30 minutes and defines a 30 minute timeframe extending back in time from time t. It can be seen that for the majority of the period encompassed by timeframe 806 the blood glucose level is trending downward. Thus, the overall calculation of the trend term will result in a negative value. Thus, by increasing the programmable value of x in order to define a longer timeframe in which to sample the trend, a more accurate calculation is made of I_((t)), thus reducing the risk of hypoglycemia to the user.

In a further embodiment, the value of x in the trend term of Equation 4 may be increased only for the falling side of blood glucose response curve 800. Thus, in this embodiment, the controller may be programmed to increase the value of x in the trend term of Equation 4 when the trend term first indicates that the blood glucose level is falling. In this manner, the better responsiveness of the shorter timeframe may be maintained while the blood glucose level is rising.

FIG. 9 shows flow diagram 900, which illustrates effects of increasing the value of x in the trend term of Equation 4 when the trend term first indicates that the blood glucose level is falling. The trend may be sampled at step 902 at time t and it may be determined whether or not the trend is falling. If the trend is not falling, the timeframe may be maintained, as shown at step 904. If the trend is falling, the timeframe may be increased, as shown at step 906. In this way, the trend control parameter of the closed-loop algorithm may be adjusted in such a way that the temporary rise in the blood glucose level may have no effect on the overall, global trend of the blood glucose level over time.

Thus, the embodiment illustrated in FIG. 8 uses the programmable trend term parameter shown in Table 1 to advantageously adjust the closed-loop algorithm such that the temporary rise in blood glucose level does not result in the delivery of a significant amount of insulin formulation and thus reduces the risks of hypoglycemia to the user.

In other embodiments of the present invention, the trend up and trend down gain control parameters may be used to advantageously adjust the closed-loop algorithm such that the temporary rise in blood glucose level does not result in the delivery of a significant amount of insulin formulation. As stated above, the trend gain control parameter β may be chosen based on observations of the insulin response of several normal glucose tolerant users.

It has been determined through experimentation that the risk of hypoglycemia may be reduced by rapidly cutting off insulin formulation delivery to the user once it is determined that the trend is falling. In some embodiments, therefore, the trend gain may be programmable and may have a greater value when the trend is falling (trend down gain) and a lesser value when the trend is rising (trend up gain).

FIG. 10 shows a flow diagram 1000 illustrating the effects of a programmable trend gain. The trend may be sampled at step 1002 at time t and it may be determined whether or not the trend is falling. If the trend is not falling, the trend up gain may be used in Equation 5, as shown at step 1004. If the trend is falling, the trend down gain may be used in Equation 5, as shown at step 1006. In this way, the trend gain control parameter of the closed-loop algorithm may be adjusted in such a way that the temporary rise in the blood glucose level may have no effect on the overall, global trend of the blood glucose level over time.

FIG. 11 illustrates why this may be advantageous in preventing the delivery of a significant amount of insulin formulation in response to the temporary, second rise in blood glucose level seen after a meal. FIG. 11 shows a graph of a human blood glucose response 1100 for a user who has ingested a meal at the point in time referred to by numeral 1102. Also shown in FIG. 11 is a timeframe, referred to by numeral 1106, wherein x=10 minutes and defines a 10 minute timeframe extending back in time from time t.

At time t₁, the trend of the blood glucose level is sampled and is determined to be rising 1104. Thus, the trend term will be some positive value. As an example, the trend term may have a value of 2 mg/dl/minute, as shown in Table 1 above. As seen in Equation 5, this value will be multiplied by the trend gain, and because it is positive, the trend up gain will be used. In this example, the trend up gain is chosen as 1.0 units/hour * (mg/dl/minute), as shown in Table 1. Thus, the derivative component of Equation 5 may be calculated as 1.0 units/hour * (mg/dl/minute) * 2 mg/dl/minute=2 units/hour. It can be seen, therefore, that because, in the present example, the trend is rising at a rate of 2 mg/dl/minute, an additional 2 units/hour of insulin formulation is added to the proportional component and the basal component of Equation 5.

In contrast, when the trend is falling, a larger value of trend gain, i.e., the trend down gain, is used. Shown in FIG. 11 is a timeframe, referred to by numeral 1110, wherein x=10 minutes and defines a 10 minute timeframe extending back in time from time t. At time t₂ the trend of the blood glucose level is sampled and is determined to be falling 1108. Thus, the trend term will be some negative value. As an example, the trend term may have a value of −2 mg/dl/minute, as shown in Table 1 above. As seen in Equation 5, this value will be multiplied by the trend gain, and because it is negative, the trend down gain is used. In this example, the trend down gain is chosen as 3.0 units/hour * (mg/dl/minute), as shown in Table 1. Thus, the derivative component of Equation 5 may be calculated as 3.0 units/hour * (mg/dl/minute) * −2 mg/dl/minute=−6 units/hour. It can be seen, therefore, that because in the present example the trend is falling at a rate of 2 mg/dl/minute, it is calculated that 6 units an hour should be subtracted from the current insulin formulation delivery rate.

In some embodiments, the trend down gain may be chosen such that the calculation of the derivative component of Equation 5 results in a high enough negative value to completely offset the other components of Equation 5 and, thus, to substantially cut off further delivery of insulin formulation during the down trend, even though the blood glucose level is currently above the set point 1116. Thus, embodiments may use a high enough value for the trend down gain such that the temporary rise in blood glucose level may have no effect, since the delivery of insulin formulation may be cut off at a time t before the temporary rise occurs. Thus, the risk of hypoglycemia to the user is reduced.

In other embodiments of the present invention, the closed-loop algorithm advantageously disables the trend term from contributing I_((t)) under certain circumstances in order to further reduce the risks of hypoglycemia to a user. In one embodiment, the trend term of Equation 5 is disabled and does not contribute to I_((t)) unless the trend is rising and the user's goal blood glucose level has been reached.

This is illustrated in flow diagram 1200 shown in FIG. 12. The blood glucose level may be sampled at step 1202 and it may be determined whether or not the user's goal (set point) has been reached. If the goal has not been reached, the trend term may be disabled, as shown at step 1204. If the goal has been reached, the trend term may be enabled, as shown at step 1206. In this way, the closed-loop algorithm may be adjusted in such a way that a significant amount of insulin formulation may not be delivered to the user unless the user's blood glucose level is both rising and, at the same time, above the user's blood glucose level set point, thus reducing the risk of hypoglycemia.

FIG. 13 illustrates one embodiment. FIG. 13 shows a graph of a human blood glucose response 1300 for a user who has ingested a meal at the point in time referred to by numeral 1302. The blood glucose level begins to rise 1304, but is still below the user's set point value 1316. Thus, in one embodiment the derivative component of Equation 5 is disabled and does not contribute to I_((t)). When the blood glucose level reaches the set point 1316 at time t, the derivative component of Equation 5 is enabled and begins to contribute to I_((t)).

Shown in FIG. 13 is a timeframe, referred to by numeral 1306, wherein x=10 minutes and defines a 10 minute timeframe extending back in time from time t. At time t the trend of the blood glucose level may be sampled to determine the difference between the blood glucose level at time t and at time t-10, as described above in relation to FIG. 11. Therefore, once the user's blood glucose level is both rising and above the set point, the trend term of Equation 4 (which is equivalent to the derivative component of Equation 5) may be calculated. An additional amount of insulin formulation determined by the calculation may then be delivered to the user to assist in metabolizing the blood glucose.

In other embodiments of the present invention, the closed-loop algorithm advantageously enables and disables the basal bate B₀ component of Equation 5, which may be a programmable control parameter (as shown in Table 1 above). In one embodiment, the basal rate component may be enabled or disabled based in part on whether the user's blood glucose level is above or below, respectively, the user's set point.

As discussed above, the basal rate component B₀ of Equation 5 represents, in some embodiments, a minimum amount of insulin formulation that would be delivered when the blood glucose level at time t is equal to or greater than the desired blood glucose level or set point and without regard to the rate at which the blood glucose level is rising or falling. Embodiments advantageously disable the basal rate component. B₀ of Equation 5 from contributing to I_((t)) when the blood glucose level falls below the set point and the trend term is falling. This may be done, for example, to substantially inhibit any further delivery of insulin formulation when the blood glucose level has fallen from a maximum value to a point below the set point.

FIG. 14 shows a flow diagram 1400, illustrating the effects of the basal rate component of Equation 5. The blood glucose level may be sampled at step 1402 and it may be determined whether or not the user's blood glucose level is below the set point. If the blood glucose level is not below the set point, the basal rate component of Equation 5 may be enabled, as shown at step 1404. If the blood glucose level is below the set point, the trend may be sampled and it may be determined whether or not the trend is falling, as shown at step 1406. If the trend is not falling, the basal rate component of Equation 5 may be enabled, as shown at step 1404. If the trend is falling, the basal rate component of Equation 5 may be disabled, as shown at step 1408. In this way, the basal rate component of Equation 5 would be enabled when the blood glucose level sampled at time t is equal to or greater than the set point value regardless of the trend direction and would be disabled when the blood glucose level sampled at time t is less than the set point value and the trend is falling.

FIG. 15 illustrates one embodiment. FIG. 15 shows a graph of a human blood glucose response 1500 for a user who has ingested a meal at the point in time referred to by numeral 1502. The blood glucose level begins to rise 1504, but is below the user's set point 1516. Thus, according to the one embodiment, even though the user's blood glucose level is below the set point 1516, the basal rate component of Equation 5 is enabled because the trend is not falling. The blood glucose level is still rising at 1506 and is now above the user's set point 1516. Thus, because the user's blood glucose level is both above the set point 1516 and rising, the basal rate component of Equation 5 is enabled. According to one embodiment, under the conditions, described above in relation to 1504 and 1506, the basal rate component of Equation 5 is enabled and contributes to

At 1508, the blood glucose level is falling, but is above the user's set point 1516. Thus, even though the user's blood glucose level is falling, it is still above the set point 1516 and, therefore, the basal rate component of Equation 5 is enabled. At 1510, the blood glucose level is still falling and is now below the set point. Thus, because the blood glucose level is both falling and below the set point, the basal rate component of Equation 5 is disabled and does not contribute to I_((t)). Therefore, one embodiment substantially cuts off any insulin formulation, including the basal rate component, when the glucose level is both falling and below the set point. In this way, embodiments reduce the risk of hypoglycemia.

Further embodiments of the present invention may include a programmable table of basal rate values. The closed-loop algorithm may be programmable to select particular basal rate values from the table to be used in calculating I_((t)) in Equation 5, for example, at particular times of the day. As an example, a different basal rate value may be selected at particular time intervals throughout the day. In one embodiment, the basal rate value may be updated every 30 minutes. In further embodiments, other control parameters within the closed-loop algorithm may be adjusted differently at different times of the day.

Thus, embodiments may advantageously adjust the basal rate based on daily events such as, but not limited to, meals, sleep, exercise, stress inducing events, ingested medications, and the like. In addition, embodiments enable the updating of basal rate values based on a particular user's historical physiological data. For example, a particular user may have a lower need for insulin at night. For that user the closed-loop algorithm may be programmed to use lower basal rate values at night.

Monitoring Biological States Other Than Blood Glucose Level

In further embodiments of the present invention, the amount and/or rate of delivered insulin formulation may modified based on inputs from sensing devices that detect other biological states in lieu of or in addition to the sensed blood glucose level. For example, it has been observed that a user's blood oxygen levels may change based on whether the user is awake or sleeping. As discussed above, sleep is an event which may significantly affect blood glucose levels in particular users. Thus, embodiments may sense the blood oxygen level of a user to determine if the user is asleep and input this information to the closed-loop algorithm in order to adjust the amount and/or delivery rate of insulin formulation based on this information.

Similarly, it has been observed that body temperature may significantly affect blood glucose levels. Thus, one embodiment includes a temperature sensor which monitors body temperature and includes this information as an input to the controller in order to adjust the amount and/or delivery rate of insulin formulation based on this information.

Further embodiments of the present invention may include a sensing device for detecting whether or not a user is exercising. In one embodiment, an accelerometer or other device suitable for detecting motion may be used to detect motion as an indicator of current physical activity. As discussed above, exercise may significantly affect blood glucose levels in particular users. Thus, information from the exercise sensing device may be input to the controller in order to adjust the amount and/or delivery rate of insulin formulation based on this information.

Referring again to FIG. 1, in one embodiment sensor 102 may sense many biological states including, but not limited to, blood, glucose level, blood oxygen level, and temperature. Sensor 102 may further include an exercise sensing device such as an accelerometer. In other embodiments, a separate blood glucose level sensor, blood oxygen level, temperature sensor and exercise sensing device may be used. Further embodiments may include sensors that detect various combinations of these and/or other biological states.

Reduction Of Accumulated Insulin Formulation

An infusion pump for the delivery of an infusion formulation according to some embodiments has a fixed pump stroke volume, i.e., there is a certain minimum value of infusion, formulation that must be accumulated before a pump stroke is executed, referred to in the present disclosure as a “pump stroke volume.” Thus, if I_((t)) is calculated on a periodic basis, for example each minute, then the calculated amount for each minute may be some fractional part of a pump stroke volume. These fractional parts may be stored, for example, in a chamber or reservoir within or adjacent to the infusion pump until an amount equal to the pump stroke volume has been accumulated. At that time, a pump stroke may be executed and the insulin formulation delivered.

The process where a pump stroke volume is accumulated is illustrated with reference to FIGS. 16A and 16B. FIG. 16A shows a graph of a human blood glucose response 1600 for a user who has ingested a meal at the point in time referred to by numeral 1602. FIG. 16B shows a magnified view of a portion of the response curve referred to in FIG. 15A by numeral 1608.

The blood glucose level begins to rise 1604. At time t₁, a first value for I_((t)) may be calculated using Equation 5. The amount of insulin formulation calculated as I_((t)) at time t₁ may be some fractional part of a pump stroke volume and may be stored in the accumulator. At time t₂, a second value for I_((t)) may be calculated. The amount of insulin formulation calculated as I_((t)) at time t₂ may also be some fractional part of a pump stroke volume and may be added to the first value stored in the accumulator. At time t₃, a third value for I_((t)) may be calculated, and so on.

At time t_(n), an nth value of I_((t)) is calculated using Equation 5. The amount of insulin formulation calculated as I_((t)) at time t_(n) is added to the accumulator, at which time the amount of insulin formulation in the accumulator is equivalent to a pump stroke volume. A pump stroke may now be executed to deliver the insulin formulation. Time t_((n)) may vary based on the pump stroke volume and the intervals at which I_((t)) is calculated.

As stated above, a process controller employing a closed-loop algorithm to control the delivery of an insulin formulation may be restricted to adding insulin formulation to the system, i.e., a body. Once insulin formulation is added to the system, normally the controller cannot retrieve it.

In further embodiments of the present invention, the accumulated volume of infusion formulation may be purged from the accumulation chamber or reservoir (also referred to in the present disclosure as the “accumulator”) when the calculation of I_((t)) yields a result which shows that the blood glucose level is falling. Thus, although once delivered the infusion formulation may not be retrievable from the body, it may be retrieved from the accumulator before the pump stroke is executed.

In one embodiment, at any time before a pump stroke is executed, the controller may issue a command to purge the accumulator. For example, once it is determined that the blood glucose level is falling and delivery of further insulin formulation is not desirable, the amounts of insulin formulation that were calculated at times t, through t_(n) while the blood glucose level was rising may be purged from the accumulator once the blood glucose level begins to fall. Thus, the accumulator may be advantageously “zeroed out.” In addition, under circumstances involving high levels of blood glucose, the accumulator may be allowed to go negative, thus delaying the effect of future increases in blood glucose levels.

Programmable Control Parameters For Bolus Safety Limits

In further embodiments of the present invention, a large amount of insulin formulation (a “bolus”) may be delivered by the infusion formulation delivery device, independently of Equation 5, when a user has a blood glucose level that is above a predefined value and is rising at or above a predefined rate, thus possibly indicating that a meal has been consumed. In other words, when the predefined criteria is met, the bolus amount may be delivered instead of a value of I(t) calculated using Equation 5.

In preferred embodiments, predefined bolus safety limits are included as control parameters for the closed-loop algorithm. In some embodiments, the bolus control parameters may be programmable in real time. Table 2 shows example bolus safety limit control parameters that may be programmable in different embodiments of the present invention. In some embodiments, all the control parameters shown in Table 2 are programmable. In one embodiment, the control parameters shown in Table 2 may be programmed in real time. Table 2 also includes example values for each control parameter.

TABLE 2 Control Parameter Value Bolus amount Up to 25 units in increments of 0.2 units; preferably 1-8 units Time between boluses One minute to 24 hours; preferably 30-60 minutes Bolus threshold 50-200 mg/dl; preferably 80-160 mg/dl Bolus trend Varies from individual to individual; typically 1-5 mg/dl/min for humans; preferably 2-4 mg/dl/min

Preferred embodiments of the present invention use the programmable control parameters shown in Table 2 to advantageously provide safety limits to be used in order to reduce the possibility of erroneously delivering a bolus by ensuring that the status of each control parameter is verified before a bolus delivery is executed by the infusion formulation delivery device. This is illustrated by flow diagram 17, shown in FIG. 17.

As discussed above, the blood glucose level is sampled at intervals, for example every minute. In some embodiments, each time the blood glucose level is sampled, a check is performed by the closed-loop algorithm to determine the status of the control parameters shown in Table 2.

In one embodiment, the closed-loop algorithm first determines if a bolus delivery feature is enabled 1702. This may be determined, for example, by comparing a predefined “bolus amount” control parameter value with zero. If the value is equal to zero, bolus delivery may be disabled 1704. If the value is greater than zero, the “time between boluses” control parameter may be checked 1706.

The “time between boluses” control parameter determines whether or not a predefined time interval has been exceeded since the last bolus delivery. If the time interval between bolus deliveries has not been exceeded, bolus delivery may be disabled 1704. If the time interval between bolus deliveries has been exceeded, the “glucose threshold” control parameter may be checked 1708.

The “glucose threshold” control parameter determines whether or not a predefined blood glucose level has been reached. If the predefined blood glucose level has not been reached, the bolus delivery feature may be disabled 1704. If the predefined blood glucose level has been reached, then the “bolus trend” control parameter may be checked 1710.

The “bolus trend” control parameter determines whether or not the blood glucose level is rising at a predefined rate. If the blood glucose level is not rising at the predefined rate, then the bolus delivery feature may be disabled 1704. If the blood glucose level is rising at the predefined rate, then the bolus delivery feature may be enabled 1712. Also, according to an embodiment of the present invention, additional signal processing may be implemented to detect a signature of a meal, which may then be used to enable the bolus feature.

Thus, embodiments advantageously provide bolus safety limits to reduce the possibility of erroneously delivering a bolus by ensuring that predefined conditions for delivery of a bolus are met by testing predefined control parameters that are programmable. Thus, the closed-loop algorithm reduces the possibility of delivering too much insulin formulation as a bolus and thus reduces the risks of hypoglycemia to the user.

Programmable Control Parameters For Maximum Insulin Formulation Delivery Amounts

In yet other embodiments of the present invention, additional safety limits may be used to ensure that no more than a predefined maximum amount of insulin formulation is stored in the accumulator at each sampling interval. For example, when the sampling interval is one minute, a limit may be set on the maximum amount of insulin formulation that may be stored in the accumulator each minute. This amount may be programmable.

Similarly, in yet a further embodiment, a limit may be set on the maximum amount of insulin formulation that may be delivered by the infusion formulation delivery device in one hour. This amount may also be programmable.

Thus, by “clamping” the maximum amount that may be stored in the accumulator at each sampling period and the maximum amount that may be delivered to the body each hour, embodiments of the present invention reduce the possibility of delivering too much insulin formulation and thus reduce the risks of hypoglycemia to the user.

Accordingly, a number of aspects and features of preferred embodiments of the closed-loop algorithm described above may provide programmable control parameters for tuning the closed-loop algorithm to more accurately determine an amount of insulin formulation to be delivered in response to a sensed blood glucose level in order to reduce the risks of hypoglycemia to a user. Additional aspects and features of preferred embodiments of the closed-loop algorithm may provide safety limits which reduce the risks of hypoglycemia to a user. The aspects and features described above may be combined to provide maximum control and safety for a user. However, the foregoing description of embodiments of the invention has been presented for the purposes of illustration and description. It is not intended to be exhaustive or to limit the invention to the precise forms disclosed. Many modifications and variations are possible in light of the above teaching.

For example, several embodiments of the closed-loop algorithm were described above in relation to a graph of a human blood glucose response for a user who has ingested a meal. These examples are meant to be illustrative and not limiting. The meal event is used as an example of an event which may lead to changes in insulin production by the pancreas of a non-diabetic person, and for which the tuning of the closed-loop algorithm using control parameters may be advantageous. However, the meal event should not be considered to be a limitation on the events which may affect glucose levels in the human body, and thus on the events for which adjustable control parameters for tuning the closed-loop algorithm may be advantageous.

Thus, the programmable control parameters may be adjusted to adjust the closed-loop algorithm to more accurately calculate the amount of insulin formulation to be delivered during or after other events which may affect the blood glucose response of a user. For example, The programmable control parameters may be adjusted to more accurately calculate the amount of insulin formulation to be delivered during or after exercise events, medication events, stress events, sleep events, and the like.

Having disclosed exemplary embodiments and the best mode, modifications and variations may be made to the disclosed embodiments while remaining within the scope of the invention as defined by the following claims. 

1-51. (canceled)
 52. A method for delivering an insulin formulation, the method comprising: taking a plurality of samples of a blood glucose level over a period of time; filtering out high frequency components of the sampled blood glucose level; comparing the filtered sampled blood glucose level to a predefined blood glucose level threshold; and disabling insulin formulation delivery if the sampled blood glucose level is less than the predefined blood glucose level threshold.
 53. The method recited in claim 52, wherein filtering comprises filtering with a finite impulse response filter.
 54. The method recited in claim 53, wherein an order of the finite impulse response filter is selectively changeable.
 55. The method recited in claim 53, wherein filtering with the finite impulse response filter comprises determining an order of the finite impulse response filter based on whether the sampled blood glucose level is falling.
 56. The method of claim 55, wherein determining the order of the finite impulse response filter comprises: increasing the order of the finite impulse response filter responsive to the sampled blood glucose level falling; and maintaining the order of the finite impulse response filter responsive to the sampled blood glucose level rising.
 57. The method recited in claim 56, wherein determining whether the sampled blood glucose level is falling or rising is based on a derivative of the sampled blood glucose level as a function of time.
 58. The method recited in claim 52, further comprising: disabling insulin formulation delivery if the amount of insulin formulation delivered in a predefined time period is more than a predefined insulin formulation amount threshold.
 59. The method recited in claim 52, further comprising: detecting a biological state; wherein said insulin formulation delivery is adjusted based on the biological state.
 60. The method recited in claim 59, wherein the detected biological state comprises sleeping, being awake, temperature, exercising, or blood oxygen level.
 61. A method for delivering an insulin formulation, the method comprising: detecting a biological state a plurality of times over a period of time; wherein the detected biological state comprises sleeping, being awake, temperature, exercising, or blood oxygen level; and adjusting an insulin formulation delivery based on the detected biological state.
 62. A system for delivering an insulin formulation comprising: a data acquisition unit for taking a plurality of samples of a blood glucose level over a period of time; a filter for filtering out high frequency components of the sampled blood glucose level; a comparator for comparing the filtered sampled blood glucose level to a predefined blood glucose level threshold; and a controller for disabling insulin formulation delivery if the sampled blood glucose level crosses the predefined blood glucose level threshold.
 63. The system of claim 62, wherein the filter is a low pass filter.
 64. The system of claim 62, wherein the filter is a finite impulse response filter.
 65. The method recited in claim 64, wherein an order of the finite impulse response filter is selectively changeable.
 66. The system of claim 64, wherein the controller is configured to determine an order of the finite impulse response filter based on whether the sampled blood glucose level is falling.
 67. The system of claim 66, wherein the controller is further configured to increase the order of the finite impulse response filter responsive to the sampled blood glucose level and to maintain the order of the finite impulse response filter responsive to the sampled blood glucose level rising.
 68. The system recited in claim 67, wherein determining whether the sampled blood glucose level is falling or rising is based on a derivative of the sampled blood glucose level as a function of time.
 69. The system recited in claim 62, wherein the controller is further configured to disable the insulin formulation delivery if the amount of insulin formulation delivered in a predefined time period is more than a predefined insulin formulation amount threshold.
 70. The system recited in claim 62: wherein the data acquisition unit is further configured to detect a biological state; wherein the controller is further configured to adjust the insulin formulation delivery based on the biological state.
 71. The system recited in claim 70, wherein the biological state detected by the data acquisition unit comprises sleeping, being awake, temperature, exercising, or blood oxygen level. 